The overall goal of this R24 project is to increase the value of rhesus monkeys (Macaca mullatta) as an animal model resource for all aspects of biomedical research. Rhesus monkeys are the most commonly used non-human primate in research. Investigators in a broad range of scientific fields, including but not limited to AIDS research, neuroscience, cardiovascular disease, research on aging, obesity, reproductive biology and many other aspects of human health depend on rhesus monkeys. Over the past ten years, genomics has become an increasingly important element of biomedical research. The Human Gene Mapping project has and will continue to transform biomedicine. However, little effort has been made to develop advanced information about the genome of rhesus monkeys. The five Specific Aims of this project will address this need by producing the first genetic linkage map of the rhesus genome. We will collect DNA samples from 700 pedigreed rhesus drawn from three NIH Regional Primate Research Centers (Specific Aim 1). The pedigrees will be selected from multiple centers to maximize analytical power for genetic linkage mapping. Then we will identify 300 highly polymorphic microsatellite loci in rhesus monkeys (Specific Aim 2). These loci will serve as the initial framework linkage map for the species, will cover all 21 pairs of rhesus chromosomes, and will have high levels of allelic variation resulting in high information content. Then, all 300 genetic markers will be genotyped in all 700 pedigreed rhesus (Specific Aim 3). We will use the new genetic data to construct a complete, 10 centimorgan linkage map, using standard analytical methods (Specific Aim 4). Finally we will develop new Bayesian statistical methods for obtaining additional information from the new data, as well as previously generated data (Specific Aim 5). All the loci to be mapped in rhesus will have first been mapped in the human genome, and most will already be mapped in baboons. Given that we will have linkage maps for three species that all employ the same set of marker loci, it is appropriate to use Bayesian influential methods to extract the maximum possible information about locus order and distance from all the data available. When completed, this project will provide valuable new genetic data and methods that will make more useful for wide range of biomedical research purposes.